1 Transects

Plant Flowers Date lon lat ele Month Year julian
Glossoloma oblongicalyx 4 2015-10-19 -78.59093 0.130838 2270 October 2015 292
Gasteranthus quitensis 2 2016-10-17 -78.59770 0.120070 1940 October 2016 291
Kohleria affinis 1 2016-12-13 -78.59534 0.126746 2110 December 2016 348
Columnea ciliata 3 2014-02-27 -78.59934 0.116682 1960 February 2014 58
Columnea medicinalis 1 2014-04-23 -78.59372 0.128700 2130 April 2014 113
Drymonia teuscheri 3 2016-07-28 -78.59245 0.129393 2200 July 2016 210

2 Interactions

3 Phylogeny

4 Traits

Plant Flowers Date lon lat ele Month Year julian
Glossoloma oblongicalyx 4 2015-10-19 -78.59093 0.130838 2270 October 2015 292
Gasteranthus quitensis 2 2016-10-17 -78.59770 0.120070 1940 October 2016 291
Kohleria affinis 1 2016-12-13 -78.59534 0.126746 2110 December 2016 348
Columnea ciliata 3 2014-02-27 -78.59934 0.116682 1960 February 2014 58
Columnea medicinalis 1 2014-04-23 -78.59372 0.128700 2130 April 2014 113
Drymonia teuscheri 3 2016-07-28 -78.59245 0.129393 2200 July 2016 210

4.0.1 Total Flowers

4.1 Peak date

As range

4.2 Infer absences

4.3 Species elevation ranges

4.4 Flowering Data Matrix

4.5 Species by transect matrix

5 Count model of species phenology

## sink("model/threshold_baseline_elev.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Occurrence at a given elevation
##     logit(psi[x]) = alpha[Plant[x]] + beta[Plant[x]] * ele[x]
##     z[x]~dbern(psi[x])
##     
##     #In Peak Flowering?
##     logit(p[x]) =  alpha_flowering[Plant[x]]
##     
##     #Observation of flowering|occurrence
##     phi[x] = z[x] * p[x]
##     Y[x] ~ dbern(phi[x])
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - phi[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(phi[x])
##     discrepancy.new[x]<-abs(Ynew[x] - phi[x])
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     ###########
##     #Prediction
##     ###########
##     
##     for(x in 1:Npreds){
##     
##     #Occurrence at a given elevation
##     logit(psi_new[x]) = alpha[Plant_new[x]] + beta[Plant_new[x]] * ele_new[x]
##     z_new[x]~dbern(psi_new[x])
##     
##     logit(p_new[x]) <-  alpha_flowering[Plant[x]]
##     
##     #Observation of flowering|occurrence
##     phi_new[x] = z_new[x] * p_new[x]
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(phi_new[x])
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##    
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Prevalence
##     alpha[j] ~ dnorm(0,0.386)
##     alpha_flowering[j] ~ dnorm(0,0.386)
## 
##     #Effect of elevation on occurrence  
##     beta[j] ~ dnorm(0,0.386)
##     } 
##     
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 5866
##    Total graph size: 36558
## 
## Initializing model

6 Get Chains

6.0.1 Evaluate convergence

6.0.2 Posterior estimates

7 Phylogeny

7.1 Attraction

## sink("model/threshold_attraction_elev.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Occurrence at a given elevation
##     logit(psi[x]) = alpha[Plant[x]] + beta[Plant[x]] * ele[x]
##     z[x]~dbern(psi[x])
##     
##     #In Peak Flowering?
##     logit(p[x]) =  e[Plant[x],Site[x],Month[x]]
##     
##     #Observation of flowering|occurrence
##     phi[x] = z[x] * p[x]
##     Y[x] ~ dbern(phi[x])
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - phi[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(phi[x])
##     discrepancy.new[x]<-abs(Ynew[x] - phi[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     ###########
##     #Prediction
##     ###########
##     
##     for(x in 1:Npreds){
##     
##     #Occurrence at a given elevation
##     logit(psi_new[x]) = alpha[Plant_new[x]] + beta[Plant_new[x]] * ele_new[x]
##     z_new[x] ~ dbern(psi_new[x])
##     
##     logit(p_new[x]) =  e[Plant_new[x],Site_new[x],Month_new[x]]
##     
##     #Observation of flowering|occurrence
##     phi_new[x] = z_new[x] * p_new[x]
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(phi_new[x])
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #Autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     for(y in 1:Months){
##     e[1:Plants,x,y] ~ dmnorm(zeros,tauC[,])
##     }
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Prevalence
##     alpha[j] ~ dnorm(0,0.386)
##     
##     #Effect of elevation on occurrence  
##     beta[j] ~ dnorm(0,0.386)
##     } 
##     
##     #Autocorrelation priors
##     gamma ~ dunif(0.1,5)
##     
##     #Strength of covariance decay
##     lambda_cov ~ dunif(0,5)
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 5926
##    Total graph size: 44893
## 
## Initializing model

8 Get Chains

8.0.1 Evaluate convergence

8.0.2 Posterior estimates

Mean phylogenetic covariance

8.1 Decay in phylogenetic attraction

8.2 Repulsion

## sink("model/threshold_repulsion_elev.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Occurrence at a given elevation
##     logit(psi[x]) = alpha[Plant[x]] + beta[Plant[x]] * ele[x]
##     z[x]~dbern(psi[x])
## 
##     #In Peak Flowering?
##     logit(p[x]) =  e[Plant[x],Site[x],Month[x]]
##     
##     #Observation of flowering|occurrence
##     phi[x] = z[x] * p[x]
##     Y[x] ~ dbern(phi[x])
## 
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(phi[x])
##     discrepancy.new[x]<-abs(Ynew[x] - phi[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     ###########
##     #Prediction
##     ###########
## 
##     for(x in 1:Npreds){
## 
##     #Occurrence at a given elevation
##     logit(psi_new[x]) = alpha[Plant_new[x]] + beta[Plant_new[x]] * ele_new[x]
##     z_new[x]~dbern(psi_new[x])
## 
##     logit(p_new[x]) <-  e[Plant_new[x],Site_new[x],Month_new[x]]
## 
##     #Observation of flowering|occurrence
##     phi_new[x] = z_new[x] * p_new[x]
## 
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(phi_new[x])
## 
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #Autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     for(y in 1:Months){
##     e[1:Plants,x,y] ~ dmnorm(zeros,tauC[,])
##     }
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##       #Prevalence
##       alpha[j] ~ dnorm(0,0.386)
##       
##       #Effect of elevation on occurrence  
##       beta[j] ~ dnorm(0,0.386)
##     } 
##     
##     #Autocorrelation priors
##     gamma ~ dunif(0.1,5)
##     
##     #Strength of covariance decay
##     lambda_cov ~ dunif(0,5)
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 5926
##    Total graph size: 42540
## 
## Initializing model

9 Get Chains

9.0.1 Evaluate convergence

9.0.2 Posterior estimates

Mean phylogenetic covariance martix

9.1 Decay in phylogenetic repulsion

10 Traits

10.1 Trait Attraction

## sink("model/threshold_attraction_elev.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Occurrence at a given elevation
##     logit(psi[x]) = alpha[Plant[x]] + beta[Plant[x]] * ele[x]
##     z[x]~dbern(psi[x])
##     
##     #In Peak Flowering?
##     logit(p[x]) =  e[Plant[x],Site[x],Month[x]]
##     
##     #Observation of flowering|occurrence
##     phi[x] = z[x] * p[x]
##     Y[x] ~ dbern(phi[x])
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - phi[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(phi[x])
##     discrepancy.new[x]<-abs(Ynew[x] - phi[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     ###########
##     #Prediction
##     ###########
##     
##     for(x in 1:Npreds){
##     
##     #Occurrence at a given elevation
##     logit(psi_new[x]) = alpha[Plant_new[x]] + beta[Plant_new[x]] * ele_new[x]
##     z_new[x] ~ dbern(psi_new[x])
##     
##     logit(p_new[x]) =  e[Plant_new[x],Site_new[x],Month_new[x]]
##     
##     #Observation of flowering|occurrence
##     phi_new[x] = z_new[x] * p_new[x]
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(phi_new[x])
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #Autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     for(y in 1:Months){
##     e[1:Plants,x,y] ~ dmnorm(zeros,tauC[,])
##     }
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Prevalence
##     alpha[j] ~ dnorm(0,0.386)
##     
##     #Effect of elevation on occurrence  
##     beta[j] ~ dnorm(0,0.386)
##     } 
##     
##     #Autocorrelation priors
##     gamma ~ dunif(0.1,5)
##     
##     #Strength of covariance decay
##     lambda_cov ~ dunif(0,5)
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 5926
##    Total graph size: 45049
## 
## Initializing model

11 Get Chains

11.0.1 Evaluate convergence

11.0.2 Posterior estimates

11.1 Decay in trait attraction

11.2 Repulsion

## sink("model/threshold_repulsion_elev.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Occurrence at a given elevation
##     logit(psi[x]) = alpha[Plant[x]] + beta[Plant[x]] * ele[x]
##     z[x]~dbern(psi[x])
## 
##     #In Peak Flowering?
##     logit(p[x]) =  e[Plant[x],Site[x],Month[x]]
##     
##     #Observation of flowering|occurrence
##     phi[x] = z[x] * p[x]
##     Y[x] ~ dbern(phi[x])
## 
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(phi[x])
##     discrepancy.new[x]<-abs(Ynew[x] - phi[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     ###########
##     #Prediction
##     ###########
## 
##     for(x in 1:Npreds){
## 
##     #Occurrence at a given elevation
##     logit(psi_new[x]) = alpha[Plant_new[x]] + beta[Plant_new[x]] * ele_new[x]
##     z_new[x]~dbern(psi_new[x])
## 
##     logit(p_new[x]) <-  e[Plant_new[x],Site_new[x],Month_new[x]]
## 
##     #Observation of flowering|occurrence
##     phi_new[x] = z_new[x] * p_new[x]
## 
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(phi_new[x])
## 
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #Autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     for(y in 1:Months){
##     e[1:Plants,x,y] ~ dmnorm(zeros,tauC[,])
##     }
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##       #Prevalence
##       alpha[j] ~ dnorm(0,0.386)
##       
##       #Effect of elevation on occurrence  
##       beta[j] ~ dnorm(0,0.386)
##     } 
##     
##     #Autocorrelation priors
##     gamma ~ dunif(0.1,5)
##     
##     #Strength of covariance decay
##     lambda_cov ~ dunif(0,5)
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 5926
##    Total graph size: 42696
## 
## Initializing model

12 Get Chains

12.0.1 Evaluate convergence

12.0.2 Posterior estimates

12.1 Decay in trait repulsion

13 Interaction

13.1 Attraction

## sink("model/threshold_repulsion_elev.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Occurrence at a given elevation
##     logit(psi[x]) = alpha[Plant[x]] + beta[Plant[x]] * ele[x]
##     z[x]~dbern(psi[x])
## 
##     #In Peak Flowering?
##     logit(p[x]) =  e[Plant[x],Site[x],Month[x]]
##     
##     #Observation of flowering|occurrence
##     phi[x] = z[x] * p[x]
##     Y[x] ~ dbern(phi[x])
## 
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(phi[x])
##     discrepancy.new[x]<-abs(Ynew[x] - phi[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     ###########
##     #Prediction
##     ###########
## 
##     for(x in 1:Npreds){
## 
##     #Occurrence at a given elevation
##     logit(psi_new[x]) = alpha[Plant_new[x]] + beta[Plant_new[x]] * ele_new[x]
##     z_new[x]~dbern(psi_new[x])
## 
##     logit(p_new[x]) <-  e[Plant_new[x],Site_new[x],Month_new[x]]
## 
##     #Observation of flowering|occurrence
##     phi_new[x] = z_new[x] * p_new[x]
## 
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(phi_new[x])
## 
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #Autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     for(y in 1:Months){
##     e[1:Plants,x,y] ~ dmnorm(zeros,tauC[,])
##     }
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##       #Prevalence
##       alpha[j] ~ dnorm(0,0.386)
##       
##       #Effect of elevation on occurrence  
##       beta[j] ~ dnorm(0,0.386)
##     } 
##     
##     #Autocorrelation priors
##     gamma ~ dunif(0.1,5)
##     
##     #Strength of covariance decay
##     lambda_cov ~ dunif(0,5)
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 5926
##    Total graph size: 42696
## 
## Initializing model

13.1.1 Evaluate convergence

13.1.2 Posterior estimates

Mean interaction covariance

13.2 Decay in interaction attraction

13.3 Repulsion

## sink("model/threshold_repulsion_elev.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Occurrence at a given elevation
##     logit(psi[x]) = alpha[Plant[x]] + beta[Plant[x]] * ele[x]
##     z[x]~dbern(psi[x])
## 
##     #In Peak Flowering?
##     logit(p[x]) =  e[Plant[x],Site[x],Month[x]]
##     
##     #Observation of flowering|occurrence
##     phi[x] = z[x] * p[x]
##     Y[x] ~ dbern(phi[x])
## 
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(phi[x])
##     discrepancy.new[x]<-abs(Ynew[x] - phi[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     ###########
##     #Prediction
##     ###########
## 
##     for(x in 1:Npreds){
## 
##     #Occurrence at a given elevation
##     logit(psi_new[x]) = alpha[Plant_new[x]] + beta[Plant_new[x]] * ele_new[x]
##     z_new[x]~dbern(psi_new[x])
## 
##     logit(p_new[x]) <-  e[Plant_new[x],Site_new[x],Month_new[x]]
## 
##     #Observation of flowering|occurrence
##     phi_new[x] = z_new[x] * p_new[x]
## 
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(phi_new[x])
## 
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #Autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     for(y in 1:Months){
##     e[1:Plants,x,y] ~ dmnorm(zeros,tauC[,])
##     }
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##       #Prevalence
##       alpha[j] ~ dnorm(0,0.386)
##       
##       #Effect of elevation on occurrence  
##       beta[j] ~ dnorm(0,0.386)
##     } 
##     
##     #Autocorrelation priors
##     gamma ~ dunif(0.1,5)
##     
##     #Strength of covariance decay
##     lambda_cov ~ dunif(0,5)
##     omega = 1
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2324
##    Unobserved stochastic nodes: 5926
##    Total graph size: 42696
## 
## Initializing model

14 Get Chains

14.0.1 Evaluate convergence

14.0.2 Posterior estimates

Mean interaction covariance martix

14.1 Decay in interaction repulsion

15 Model Comparison

15.1 Predicted elevation ranges

The probability of occurrence.

15.2 E: The effect of autocorrelation on mean flowering intensity

15.2.1 E: The effect of autocorrelation

16 By site

16.1 1300m - 1500m

16.2 1500-1700m

16.3 1700-1900

16.4 1900-2100m

16.5 2100-2300m

16.6 2300m - 2500m

## Omega: The magnitude of the effect of autocorrelation on mean flowering occurrence

16.7 Gamma: The variance of the effect of autocorrelation on mean flowering occurrence

16.8 Lambda: The decay in autocorrelation effect

16.9 Decay in autocorrelation effect

17 Model Fit

17.1 Bayesian pvalue

## # A tibble: 6 x 2
##   Model                       p
##   <chr>                   <dbl>
## 1 interaction_attraction  0    
## 2 interaction_repulsion   0    
## 3 phylogenetic_attraction 0.348
## 4 phylogenetic_repulsion  0    
## 5 trait_attraction        0.586
## 6 trait_repulsion         0

17.2 Overall

Model mean lower upper
interaction_attraction 0.4544230 0.4466245 0.4667826
interaction_repulsion 0.4275323 0.4143349 0.4430686
phylogenetic_repulsion 0.4184129 0.4100661 0.4257291
trait_repulsion 0.4124492 0.3992973 0.4232168
trait_attraction 0.1387448 0.1290637 0.1478888
phylogenetic_attraction 0.1370254 0.1254357 0.1487678

17.2.1 Without baseline

17.3 By Species

Without baseline

Zoom in

18 Prediction

18.0.1 Tables

Model mean lower upper
baseline 0.3095170 0.2517007 0.3639456
trait_attraction 0.2306156 0.2091837 0.2551020
phylogenetic_attraction 0.2303265 0.2057823 0.2551020
interaction_attraction 0.2267075 0.2023810 0.2500000
interaction_repulsion 0.2206259 0.1955782 0.2482993
trait_repulsion 0.2185612 0.1955782 0.2432823
phylogenetic_repulsion 0.2179728 0.1955782 0.2398810